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SSAI Launches Framework to Guide Workforce Development in Artificial Intelligence and Machine Learning
In keeping with our ongoing commitment to acquire and build capabilities in the most advanced technological fields, SSAI has developed a comprehensive framework to guide workforce development in Artificial Intelligence and Machine Learning (AI/ML).
By continuously looking for opportunities to improve the areas of greatest concern and value to our customers and staff, we identified AI and ML as critical focus areas – and subsequently created a learning program to advance the knowledge of our organization. This program was designed using a collaborative approach that facilitated engagement and participation at all levels of the company, including senior leaders, project managers, AI/ML experts, and prospective learners.
The cornerstone of this program is our Deep Learning Academy: a multifaceted approach aimed at growing our pool of AI/ML experts quickly and establishing a thriving community of practitioners. Applicants who are selected to take their capabilities to the next level get a year-long, SSAI sponsored learning experience that begins with intensive coursework, followed by real-world projects that are relevant to our Earth and environmental science customers.
Part 1 of the program involves learners earning the certificate from Andrew Ng's highly regarded Deep Learning Specialization on Coursera, which includes 5 courses:
1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models
Each course takes place over several weeks, having learners watch lecture videos and complete graded quizzes, assignments, and labs. Advisors are available for one-on-one mentoring, and learners engage with each other for peer support via dedicated channels in Microsoft Teams.
Part 2 involves learners developing solutions to two projects designed by our advisors. In addition to paid time for project work, SSAI provides computing resources via Amazon SageMaker for model development and training. The projects include Jupyter notebooks and code templates that conceal the complexities of interacting with the underlying infrastructure. With this framework learners can hit the ground running, immediately applying what they've learned to the problem at hand rather than struggling with the details of AWS and SageMaker. Both projects are also framed as a competition between teams, with Kaggle allowing learners to submit and track progress on key metrics.
SSAI sponsorship includes vendor training fees; paid learner and mentor time; cloud infrastructure; community and collaboration tools; as well as a monitoring and evaluation framework. For those interested employees who require prerequisite knowledge to be successful, we also provide the ML fundamentals training needed for the full range of roles important in ML projects – paving the way for future cohorts in the Academy.
In summer 2021, SSAI successfully completed its first full year workshop. Currently SSAI AI/ML subject matter experts are preparing the application call to fill seats for another full year offering, which is scheduled to begin in mid-January.
To find out more about this program, contact Brandon Smith, the AI/ML Programs Lead at email@example.com.